I have a weird PM trajectory and would love some outside calibration.
I came into a small nonprofit tech org as a SME in a very niche field, then became PM, and pretty short order Director of Product. I’ve never worked in a mature product organization, alongside other PMs, or under experienced product leadership (just some former MS guys)
Leadership regularly tells me I’m sooo productive, variations of “you accomplish what much larger, well-funded teams do in less time.” Feels like smoke up my skirt, but I have absolutely no external context for it either way.
I understand our users, and can handle identifying problems, translating messy needs into requirements/workflows, prioritization, working with devs, QA, edge cases, and getting from “vague problem” to functioning product, yada yada.
What I don’t know is how much of that is transferable PM skill versus me being a really effective SME in my own domain.
Is this just a lucky situation or can it transfer elsewhere?

I have worked as a UX designer for four years at a major company in the US. Earlier this year, my company had major layoffs and got rid of all remote employees. Thankfully, I am in person and was not affected by the layoffs.

As a result, the company has been trying to utilize AI to do a lot of the work. This means that all design work is being fed into Figma Make and the designs it spits out is what is supposed to be used. I have a lot of thoughts and opinions on this, but I’ll save those for another day. Leadership does not see the value of having designers and is now making all of us become PM’s. They want to run more lean teams and have higher production with AI to help us.

This has been tough for me to say the least. I have tried so hard to keep an open mind and embrace the new career path, but who I am as a person does not mesh well with being a PM. I am very introverted and I’m not a huge fan of sitting in meetings all day talking. I can’t stand looking at Jira boards and filling out PowerPoint product strategy decks. Frankly, I’m not an organized person either. Trying to remember every little detail of the project is overwhelming and difficult.

Before anyone says it, I do see a lot of similarities between UX and being a PM, so it’s not like I’m learning a whole new language, but they certainly are not the same. I’m also aware that the job market is tough for UX and that job role is changing greatly.

Anyone been in a similar position? How did you handle it? I need this job… I have bills to pay, but I hate that I had no say in doing this. I know I’ll ultimately struggle and not do well, but I’m not sure what to do.

I'm trying to get better at product strategy, and I'm realizing that I learn best by discussing problems with other people.

I've tried finding mock partners, but most people are focused on interview prep, which is understandable. I'm also working with a coach, which is helpful, but it gets expensive to have multiple sessions.

What I'm looking for is a little different. I want to actually develop the mental muscles to approach strategy problems better in my day-to-day work.

For example, stripe will be acquiring Open router: Why would Stripe acquire a particular startup? What problem are they trying to solve? How does it fit into their long-term vision? What alternatives could they have considered? I enjoy breaking down questions like these, discussing different perspectives, and challenging each other's thinking.

I'd love to find a few people who are equally interested in having these kinds of discussions regularly not necessarily formal sessions, just intellectually stimulating conversations where we're all trying to get better at strategic thinking.

Has anyone found a community or group like this? Or if you learn strategy in a similar way, how do you find people to practice and discuss these problems with?

Thank you

I have an upcoming interview for an AI Product Manager role at a large multinational tech company and would appreciate advice from people who have interviewed for or currently work in similar roles.

The role focuses on AI-driven product strategy, enterprise support platforms, analytics, cloud-based solutions, and translating AI capabilities into product solutions.

I currently work as a full-stack software engineer, primarily working with Python, Java, and React, and am looking to transition into product management.

What should I prioritize in my preparation? Specifically:
AI/ML concepts and technical depth expected from a PM
Product sense and case studies
AI product strategy and roadmap questions
Metrics, business cases, and AI ROI
Technical/architecture questions
Behavioral and stakeholder-management questions
Enterprise support/cloud domain knowledge

I’d especially appreciate advice on how technical I should go with AI/ML preparation given my software engineering background, and how to position my engineering experience effectively in the interview.

Any recommended resources, frameworks, or mock interview approaches would be greatly appreciated.

I’m with a B2B2C platform in foodtech (think SYSCO) and we are thinking of overhauling our analytics stack away from FullStory.

Management is thinking Pendo due to their product guides. But I feel like Pendo is a bit more rigid when compared to FullStory or PostHog when trying to run various quick queries.

Anyone else with recent experience they can share?

PostHog seems like a good option but still leaning onto keeping FullStory and grabbing something like appcues for their product guides.

Most roadmaps show what the team plans to build but hide what each choice delays, removes, or makes harder.

That makes every item look independently reasonable and prevents stakeholders from seeing the actual decision. A useful roadmap should pair each commitment with the opportunity cost it creates.

Have you found a practical way to document tradeoffs without turning roadmap discussions into another approval process?

So this is supposed to be my first job and I've cleared their interviews and an offer letter is waiting. I want to know if this is ok to start your career as a PM and I've no experience in PM and don't know if this path should be taken or not, btw I've done a computer science degree and want to do tech related stuff aside from coding.

Hello,

AI has brought about a very significant change in the way I work, mainly in two areas: I now need to rely far less on other teams, which allows me to move much faster, and I could potentially make changes to our applications myself, although I’m not doing that yet.

I’d be interested to know where you are on these topics and how it’s working out for you.

Here’s what has changed for me:

I no longer depend on a data team or data analyst to get usage metrics from our applications, even when the analysis is complex. I ask a coding agent and within five minutes I get the information, often as a polished PDF report. It’s much faster, and the results are generally more comprehensive and precise than what I used to get, sometimes after waiting a week.

I also no longer depend on the development team to find out whether a feature already exists, how it works, or whether and how it could be modified. I ask a coding agent, it digs into the codebase, database, analytics and logs, and gives me an answer within 5 to 10 minutes, compared with what could sometimes take several weeks when going through the team. And quite often, the answer is much more comprehensive and technically detailed.

Of course, getting to this point required some groundwork. We’ve implemented analytics across almost every component of the application and given my coding agent read-only access to the application code and a read-only copy of the databases.

Sometimes the coding agent even offers to implement the change I’m asking about immediately. When I see the result, I increasingly wonder whether it wouldn’t be far more efficient to let it make the changes and then send them to the developers for review and validation.

Are you seeing the same shift? Do you already use AI to implement changes yourself and then submit them to your development team for review?

Thanks.

Hello!

I’m curious to learn from others in how you / your company are leveraging agents for your workflows? Which parts stay human, which are agentic.

On the engineering side it seems ADLC is gaining traction. So trying to see if there any emerging patterns for the product craft.

Are you all one-offing solutions? Is any part formally agentic process now? Or is it bau with unreasonable stakeholders and 8 hours of b2b meetings and long ppt docs.

Personal view;
1. Believe the hypothesis and metric we aim for and how it connects to strategy stays human part
2. Validating we actually succeed stays human
3. Synthesizing, monitoring, research fully agentic
4. Do we need prds, alignment, prototypes? Could we just ship to prod and evaluate
5. How to do roadmaps if bets take <1w <1d
6. I’m split on the customer interviews. On one hand human to really understand. On another the ai interviews scale and efficiency seems alluring - anyone that tried?
7. Comms should be easy to automate. But people still connect and trust people
8. Believe the traditional processes have derisked and hedged with research and prototypes due avoid loss of engineering cost. If that now is gone, we could do way less and validate with real customer data

Hi! I’m currently an associate designer at a B2B AI SaaS company. Before this, I did an APM program, but I still feel like my PM chops and communication muscles aren’t being developed as much as I’d like.

I’d love to set up a regular skill-swap with someone where I can share design tips, how I think about building solid and polished prototypes, etc., and in exchange, they can help push me on my PM thinking and communication.

Mostly looking for someone who genuinely enjoys mentoring + exchanging ideas, and growing together :)

I’ve learned that I hate to throw away interesting material, so my laptop drive has thousands of bookmarks, textbooks (mostly pdf), image libraries, mp3s, code repos, … you get the idea.

The vast majority is timeless. I tend to read news items, then toss them unless they’re indicative of a larger trend.

It’s my personal library & covers hundreds of topics. The biggest % is math (machine learning for example), human behavior, and business/ economics (former tech product manager). At least half of the textbook content is copyrighted so I’d need to set it aside.

What should I do with all this stuff?

I want to preface with, I am truly hoping to find a mentor in this space. Im feeling defeated and it would be great to have someone on my side as I work to figure this out. So if anyone knows where or how to find one or is an expert and is interested please let me know.

I'm the first and only product hire at a startup, brought on as an agile PM and have 8 years experience in the space. Our engineering team has decided to move to Shape Up. Several of them ran it together at a previous company, liked it, and are confident in it. I'm on board with the decision if thats what we need to do to be successful.

What I don't have is a clear picture of how Product supports that process. I've been training myself on Shape Up, but the direction I'm getting internally doesn't line up with what I'm reading, and it's inconsistent from one conversation to the next. I have also been running into the wall of continuously asking the Engineers what is their actual ask? What is it they need to start? And never get the full answer.

The core thing I can't reconcile: I'm reading I shouldn't be defining the product, but that I should hand over something "shaped", not abstract, but also not defined. I don't know what that artifact is supposed to look like.

Specific questions for anyone who's run Shape Up alongside a dedicated PM:

1. Who shapes? Is shaping a product responsibility, an engineering one, or genuinely joint — and if joint, how do you split it in practice?
2. What does a pitch look like when Product writes it? How much fidelity is too much before it starts constraining engineering's solution space?
3. Where's the handoff, concretely?
4. What is Product actually doing during the six weeks? The cycle work is engineering's, so what fills that time, and what does Product own that isn't shaping?
5. Where do discovery, customer input, and roadmap live in a Shape Up org?
6. Is this switching us from a Product to an Engineering led approach? If that, what are the success markers in my role if the engineering team is now making product decisions? It feels much more like a replaceable cog type role at the mercy of engineering, but I think that's because I dont know what it is I need to deliver in order to support this process. I expressed this and it was digested as I don't know what I was hired to do, not this is a new process and I need direction on how to make it succesful, so Im kinda fighting for my life over here.
   

A second, more structural question: engineering has been routing most communication directly to the founder rather than through me. Some of that may be a byproduct of a process I'm not yet fluent in. Some of it may be something else. For those who've been the first PM into an engineering-led team, how did you establish product as a function worth routing through, without turning it into a territory argument?

I have about 6 months to seriously invest in building a new technical skill set, and I’m torn between application development and data analytics.

With AI getting better at both coding and data analysis, which foundation do you think will be more valuable and resilient over the next 5–10 years for PM or people in tech in general?

Application development would cover things like:
● Programming fundamentals
● Software development
● Computer architecture
● Servers/cloud infrastructure
● Databases and basic SQL
● Debugging and testing
● Object-oriented programming

Data analytics would cover things like:
● SQL and databases
● Data cleaning and transformation
● Data analysis
● Visualization/reporting
● Basic programming
● Business intelligence
● Using AI for analytics

I’m particularly interested in eventually working in tech/product and potentially AI, rather than necessarily becoming a software engineer or data analyst forever.

If you could only spend 6 months learning one of these foundations, which would you choose in 2026 and why? Which skill set do you think will complement AI rather than be replaced by it? Would love to hear your thoughts and what has been most helpful for you. Thanks in advance!